From c180f1dd77bc37e1fb3b69a8501f1b3530b69129 Mon Sep 17 00:00:00 2001 From: Joaquin Torres Bravo Date: Thu, 9 May 2024 16:23:24 +0200 Subject: [PATCH] tuned PRE under --- model_selection/hyperparam_tuning.py | 8 ++++---- model_selection/output/hyperparam_pre_UNDER.xlsx | Bin 0 -> 5353 bytes 2 files changed, 4 insertions(+), 4 deletions(-) create mode 100644 model_selection/output/hyperparam_pre_UNDER.xlsx diff --git a/model_selection/hyperparam_tuning.py b/model_selection/hyperparam_tuning.py index 042ab11..e688b7c 100644 --- a/model_selection/hyperparam_tuning.py +++ b/model_selection/hyperparam_tuning.py @@ -143,7 +143,7 @@ if __name__ == "__main__": # Store each df as a sheet in an excel file sheets_dict = {} for i, group in enumerate(['pre']): - for j, method in enumerate(['over_']): #['', '', 'over_', 'under_'] + for j, method in enumerate(['under_']): #['', '', 'over_', 'under_'] # Get dataset based on group and method X = data_dic['X_train_' + method + group] y = data_dic['y_train_' + method + group] @@ -153,7 +153,7 @@ if __name__ == "__main__": # Save results: params and best score for each of the mdodels of this method and group hyperparam_df = pd.DataFrame(index=list(models.keys()), columns=['Parameters','Score']) for model_name, model in models.items(): - print(f"{group}-{method_names[2]}-{model_name}") + print(f"{group}-{method_names[3]}-{model_name}") # Find optimal hyperparams for curr model params = hyperparameters[model_name] search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=8, scoring='precision') @@ -162,11 +162,11 @@ if __name__ == "__main__": hyperparam_df.at[model_name,'Score']=round(search.best_score_,4) # Store the DataFrame in the dictionary with a unique key for each sheet - sheet_name = f"{group}_{method_names[j]}" + sheet_name = f"{group}_{method_names[3]}" sheets_dict[sheet_name] = hyperparam_df # Write results to Excel file - with pd.ExcelWriter('./output/hyperparam_pre_OVER.xlsx') as writer: + with pd.ExcelWriter('./output/hyperparam_pre_UNDER.xlsx') as writer: for sheet_name, data in sheets_dict.items(): data.to_excel(writer, sheet_name=sheet_name) diff --git a/model_selection/output/hyperparam_pre_UNDER.xlsx b/model_selection/output/hyperparam_pre_UNDER.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..555c86765aa5f80a4a5ab92f37637701bf548a03 GIT binary patch literal 5353 zcmZ`-2UJtrwhdLP5{e)lDbjmyQluk-v`81or4xEb5knJ@4oZhmR7yZXuYu4(dY2+a zKtOr~1bOlL-}B0UFB#`#oG~)jS$mGT)?Rz+YT@8g0ssI4z*Oj4kg5riBoy;hiMhxy zmo3Cb*8}3_De&0Mjo;73`TooM#O;D)_bxRz=Uol=n1z_fVN1P%>JsAdw#q(X=Rx|& zxH;Jc!I%cBJ`jr4S1cS=`~=vm2B;xc?NA`=SiB=;mfe8CZ-z5>0`Xsq%%C-Kk}ViiiU4t z^6bX~0D%9@*9PJN{+V!L0#c_#kR;p^{H57Dor+9vl6XvqnMH^Y#i!ZCm#+asy_ugI zOD)4ex^$mhp3fK+Tvw_E=Gf2)CUzn=%vX|zAEz)xh62>jLUanPna|0-&TdeW4%J2E z9!1Qux^=lCS-4>8NX~%{=K&o}m>u{eW{^#gwn8x?n><;CkUCYc;Rw=^JYbu%t?g}Y z?A0GdC(iw8XLBEHB3~foG5 zqh@OrY{6@m}+!yR@?JCt1@!8tq1pyQJ5c-JW&i+*+VWh=4f^+fJir%M*&;#p*v8`xd;om0_Pr z6l?lm%*l6t>}6C}*{~mJ%l?Cij`FCkl3}kc2aY=z>FSGEGksjAl$#+U&h zH=oIYQJho@t~2js+8Kt%c}255&{wG_;E%3O+)0PgTdAU|9$E2t*8vi!5pCTdxMIzV zrS`;8v-rs&u4-Lcg7lte$7-Q0ogd*B$7zkd1tI}i!O|a`lAk)1U={B)W(*~rdv2`e z6emsHSNh=A#7nsUoz?Vl#2r@|3J+;|JEfo@G&-S>U3h5slxRyw){e<2wZeR8Xf->= zgSBxU4GKm-P;+ISdfQfYdn#F#O(my?uVED^S*<}TOmUYWEBuwCEvltustpt)TXPgJpn1!B@snRZIdVcmCasGa-P;~@t*z^&W2BN}BqgfPv zC&t@3>6g;BUzRD$KtVp3OpLD>p2o&O{#NSAeRYZ-JcYAAGRGA5k_Fh#G!x<+s#uG? 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